U.S. patent application number 16/548333 was filed with the patent office on 2021-02-25 for synthetic wavelengths for endpoint detection in plasma etching.
This patent application is currently assigned to Tokyo Electron Limited. The applicant listed for this patent is Tokyo Electron Limited. Invention is credited to Yan CHEN, Xinkang TIAN, Vi VUONG.
Application Number | 20210057195 16/548333 |
Document ID | / |
Family ID | 1000005381780 |
Filed Date | 2021-02-25 |
United States Patent
Application |
20210057195 |
Kind Code |
A1 |
CHEN; Yan ; et al. |
February 25, 2021 |
SYNTHETIC WAVELENGTHS FOR ENDPOINT DETECTION IN PLASMA ETCHING
Abstract
Described is a method for determining an endpoint of an etch
process using optical emission spectroscopy (OES) data as an input.
OES data is acquired by a spectrometer in a plasma etch processing
chamber. The acquired time-evolving spectral data is first filtered
and de-meaned, and thereafter transformed into transformed spectral
data, or trends, using multivariate analysis such as principal
components analysis, in which previously calculated principal
component weights are used to accomplish the transform. Grouping of
the principal components weights into two separate groups
corresponding to positive and negative natural wavelengths, creates
separate signed trends (synthetic wavelengths).
Inventors: |
CHEN; Yan; (Cupertino,
CA) ; TIAN; Xinkang; (Fremont, CA) ; VUONG;
Vi; (Fremont, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Tokyo Electron Limited |
Tokyo |
|
JP |
|
|
Assignee: |
Tokyo Electron Limited
Tokyo
JP
|
Family ID: |
1000005381780 |
Appl. No.: |
16/548333 |
Filed: |
August 22, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H01L 21/3065 20130101;
H01J 37/32963 20130101; G06F 17/16 20130101; H01L 21/67063
20130101 |
International
Class: |
H01J 37/32 20060101
H01J037/32; G06F 17/16 20060101 G06F017/16; H01L 21/3065 20060101
H01L021/3065; H01L 21/67 20060101 H01L021/67 |
Claims
1. A method for determining etch process endpoint data in a plasma
processing system, the method comprising: performing plasma etch
process runs in a plasma processing chamber of an etch processing
system; acquiring optical emission spectroscopy (OES) data from the
plasma processing chamber during one or more etching processes;
performing a multivariate data analysis on the OES data to generate
synthetic OES data from the OES data by grouping emitted
wavelengths; and using the synthetic OES data for later use in an
in-situ determination of the etch process endpoint.
2. The method of claim 1, where the generating synthetic OES data
comprises obtaining a transformed OES data vector [T], wherein
[T]=([X]-[Savg])[P], where [X] is an OES data matrix, [P] is a
weights vector, and [Savg] is an n.times.m mean OES data matrix,
whose each element is computed as an average value of n elements of
the corresponding column of [X]avg, which is an n.times.m average
OES data matrix, whose each element is computed as an average of
corresponding elements of the OES data matrix [X] over k etch
process runs, n corresponding to instants in time when OES data is
taken and m corresponding to the number of measured light
intensities in the plasma processing chamber by a detector.
3. The method of claim 2, wherein the generating synthetic OES data
comprises grouping wavelengths corresponding to positive and
negative weights.
4. The method of claim 3, wherein the weights vector [P] is
determined by: calculating eigenvectors and eigenvalues of a
covariance matrix associated with matrix [X]; ordering the
eigenvalues in descending order, the eigenvalues representing the
weights vector [P]; and setting a positive weights vector [P+] by
setting all negative components in [P] to zero and setting a
negative weights vector [P-] by setting all positive components in
[P] to zero and taking the absolute value thereof.
5. The method according to claim 4, further comprising: obtaining a
transformed OES data vector [T+] or [T-], wherein
[T+]=([X]-[Savg])[P+],[T-]=([X]-[Savg])[P-].
6. The method according to claim 5, further comprising: selecting a
functional form involving elements of the transformed OES data
vector [T+], or [T-], and computing a time evolution of the
selected functional form.
7. The method according to claim 6, further comprising: computing a
time derivative of the selected functional form and computing a
time evolution of the time derivative of the selected functional
form.
8. The method according to claim 7, wherein the functional form
comprises [T+], [T-], the ratio [T+]/[T-], a power of the ratio
[T+]/[T-], or a single element of the transformed OES data vector
[T+] or [T-] or any mathematical form using [T+] and/or [T-].
9. The method of claim 1, further comprising performing k plasma
etch process runs in the plasma processing chamber, where k is an
integer greater than zero, each of the k plasma etch process runs
comprising: loading a substrate to be processed to the plasma
processing chamber, the plasma processing chamber comprising a
spectrometer having a detector comprising m pixels, each pixel
corresponding to a different light wavelength; forming a plasma in
the plasma etch processing chamber; and acquiring OES data from the
plasma processing chamber during one or more etching processes, and
forming an OES data matrix [X] for each of the k plasma etch
process runs.
10. The method of claim 9, further comprising computing an
n.times.m average OES data matrix [X]avg, wherein each element is
computed as an average of corresponding elements of the OES matrix
[X] over the k etch process runs; filtering noise from the average
OES data matrix [X]avg; truncating each OES data matrix [X] and
[X]avg, where data acquired during plasma startup and for times
beyond an etch process endpoint is discarded; calculating an
n.times.m mean OES data matrix [Savg], where each element is
computed as an average value of n elements of the corresponding
column of [X]avg; subtracting [Savg] from matrix [X], for each k,
to de-mean the OES data.
11. The method according to claim 2, wherein after forming the OES
data matrix [X] for each of the plasma etch process runs, and
before computing the n.times.m average OES data matrix [X]avg, the
method normalizes the OES data matrix [X].
12. The method according to claim 11, wherein the OES data matrix
normalization comprises selecting a reference snapshot at time R,
xR,j, and then dividing every OES data by the reference snapshot,
xi,j=xi,j/xR,j.
13. The method according to claim 12, wherein the reference
snapshot is a single time snapshot or a snapshot averaged over a
period of time.
14. The method according to claim 11, wherein the OES data matrix
normalization comprises selecting a reference wavelength R, and
then dividing every OES data by the intensity at the reference
wavelength xi,j=xi,j/xi,R.
15. The method according to claim 14, wherein the reference
wavelength is a single wavelength or an average of band
wavelengths.
16. A method for determining etch process endpoint data in a plasma
processing system, the method comprising: performing plasma etch
process runs in a plasma processing chamber of an etch processing
system; acquiring optical emission spectroscopy (OES) data from the
plasma processing chamber during one or more etching processes;
performing a multivariate data analysis on the OES data to generate
synthetic OES data from the OES data by grouping emitted
wavelengths corresponding to positive and negative weights
associated with natural wavelengths; and using the synthetic OES
data for later use in an in-situ determination of the etch process
endpoint.
17. The method of claim 16, wherein the multivariate data analysis
is performed using independent component analysis.
18. The method of claim 16, wherein the multivariate data analysis
is performed using a supervised multivariate data analysis method,
the supervised multivariate data analysis method including support
vector machine regression.
19. The method of claim 16, further comprising: obtaining a
transformed OES data vector [T+] or [T-], wherein
[T+]=([X]-[Savg])[P+],[T-]=([X]-[Savg])[P-], wherein [X] is an OES
data matrix, [P+] is a positive weights vector, [P-] is a negative
weights vector, and [Savg] is an n.times.m mean OES data matrix,
whose each element is computed as an average value of n elements of
the corresponding column of [X]avg, which is an n.times.m average
OES data matrix, whose each element is computed as an average of
corresponding elements of the OES data matrix [X] over k etch
process runs, n corresponding to instants in time when OES data is
taken and m corresponding to the number of measured light
intensities in the plasma processing chamber by a detector.
20. The method according to claim 19, further comprising: selecting
a functional form involving elements of the transformed OES data
vector [T+], or [T-], and computing a time evolution of the
selected functional form.
21. The method according to claim 20, further comprising: computing
a time derivative of the selected functional form and computing a
time evolution of the time derivative of the selected functional
form.
22. The method according to claim 21, wherein the functional form
comprises [T+], [T-], the ratio [T+]/[T-], a power of the ratio
[T+]/[T-], or a single element of the transformed OES data vector
[T+] or [T-] or any mathematical form using [T+] and/or [T-].
Description
RELATED APPLICATIONS
[0001] This application is related to the U.S. Pat. No. 9,330,990
('990), titled "Method of endpoint detection of plasma etching
process using multivariate analysis" and U.S. Pat. No. 10,002,804,
titled "Method of endpoint detection of plasma etching process
using multivariate analysis".
BACKGROUND
Technical Field
[0002] The present application relates to a method and system for
controlling the process of etching a structure on a substrate, for
example, in semiconductor manufacturing. More particularly, it
relates to a method for determining an endpoint of an etch process
of the substrate.
Description of the Related Art
[0003] Plasma etch processes are commonly used in conjunction with
photolithography in the process of manufacturing semiconductor
devices, liquid crystal displays (LCDs), light-emitting diodes
(LEDs), and some photovoltaics (PVs). Generally, a layer of a
radiation-sensitive material, such as photoresist, is first coated
on a substrate and exposed to patterned light to impart a latent
image thereto. Thereafter, the exposed radiation-sensitive material
is developed to remove exposed radiation-sensitive material (or
unexposed, if negative tone photoresist is used), leaving a pattern
of radiation-sensitive material which exposes areas to be
subsequently etched, and covers areas where no etching is desired.
During the etch process, for example, a plasma etch process, the
substrate and radiation-sensitive material pattern are exposed to
energetic ions in a plasma processing chamber, so as to effect
removal of the material underlying the radiation-sensitive material
in order to form etched features, such as vias, trenches, etc.
Following etching of the features in the underlying material, the
remainder of the radiation-sensitive material is removed from the
substrate using a stripping process, to expose formed etched
structures ready for further processing.
[0004] In many types of devices, such as semiconductor devices, the
plasma etch process is performed in a first material layer
overlying a second material layer, and it is important that the
etch process be stopped accurately once the etch process has formed
an opening or pattern in the first material layer, without
continuing to etch the underlying second material layer.
[0005] For purposes of controlling the etch process, various types
of endpoint control are utilized, some of which rely on analyzing
the chemistry of the gas in the plasma processing chamber in order
to deduce whether the etch process has progressed, for example, to
an underlying layer of a different chemical composition than the
chemical composition of the layer being etched. Other processes may
rely on direct in-situ measurements made of structures being
etched. In the former group, optical emission spectroscopy (OES) is
frequently used to monitor the chemistry of the gas in the plasma
processing chamber. The chemical species of the gas in the plasma
processing chamber are excited by the plasma excitation mechanism
being used, and the excited chemical species produce distinct
spectral signatures in the optical emission spectrum of the plasma.
Changes in the optical emission spectrum due to, for example,
clearing of a layer being etched, and exposing of an underlying
layer on the substrate, can be monitored and used to precisely end
the etch process, i.e., reach the endpoint, so as to avoid etching
of the underlying layer or formation of other yield defeating
defects, such as undercuts, etc.
[0006] Depending on the types of structures being etched and the
etch process parameters, the change of the optical emission
spectrum of the plasma at the endpoint of the etch process may be
very pronounced and easy to detect, or conversely, subtle and very
difficult to detect. For example, etching of structures with a very
low open ratio can make endpoint detection difficult using current
algorithms for processing OES data. Improvements are therefore
needed to make etch endpoint detection based on OES data more
robust in such challenging etch process conditions.
SUMMARY
[0007] A feature of the present application relates to a method for
determining an etch process endpoint in an etch process, where, at
the endpoint, the etch process is stopped accurately once the etch
process has formed an opening or pattern in a first material layer,
without continuing to etch the underlying second material
layer.
[0008] In one non-limiting embodiment, OES data is acquired for
different etch processing runs, to obtain OES data matrices,
average OES data matrices, and a mean OES data matrix. This data is
used so that a multivariate model can be established of the
acquired OES data. Once the multivariate model of the OES data has
been established, it is used subsequently for an in-situ etch
endpoint detection.
[0009] An analysis grouping wavelengths of similar behavior is used
to determine a weights vector P to transform the OES data vector
into the trend domain. Preferentially, by grouping the principal
components weights into two separate groups corresponding to
positive and negative natural wavelengths, separate signed trends
(synthetic wavelengths) are created.
[0010] Having determined the synthetic wavelengths, during the
in-situ etch endpoint detection, functional forms of time evolving
values of the synthetic wavelengths are plotted vs. time to
determine an endpoint of the etch process.
[0011] For example, in one embodiment, the time evolution of a
ratio of synthetic wavelengths or the time evolution of the time
derivative of a ratio of synthetic wavelengths is calculated.
[0012] However, in other embodiments, any other functional forms
may be calculated, such as, squares of the ratio of the synthetic
wavelengths or just a single signed synthetic wavelength or just a
natural wavelength trend.
[0013] In a further non-limiting embodiment, to compensate for OES
drift between different wafers, the normalized OES spectrum is used
in a principal components analysis (PCA) method.
[0014] After the time-evolving trend variable has been calculated,
a decision is made on whether an endpoint has been reached. If
indeed an endpoint has been reached, the etch process ends,
otherwise the etch process is continued, and continuously monitored
for etch endpoints.
[0015] The generation of synthetic wavelengths enables similar
trending as the natural wavelengths for the endpoint detection, but
with higher signal to noise ratio (SNR) endpoint signals.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The application will be better understood in light of the
description which is given in a non-limiting manner, accompanied by
the attached drawings in which:
[0017] FIG. 1 is a schematic of an exemplary plasma etch processing
system with a light detection device including a spectrometer used
for acquisition of OES data, and a controller implementing the etch
endpoint detection method described herein.
[0018] FIG. 2 is an exemplary flowchart of the method of preparing
etch endpoint data for later in-situ etch point detection using
multivariate analysis.
[0019] FIG. 3 is an exemplary flowchart of the method of preparing
etch endpoint data for later in-situ etch point detection using PCA
analysis.
[0020] FIG. 4 is an exemplary flowchart of the method of in-situ
etch endpoint detection.
[0021] FIG. 5 shows an exemplary graph of time evolution of a time
derivative of a trend variable functional form involving the ratio
of synthetic wavelength trends and single wavelength trends.
[0022] FIG. 6A shows an exemplary graph of time evolution of a
trend variable functional form involving a ratio of synthetic
wavelength trends.
[0023] FIG. 6B shows an exemplary graph of time evolution of the
time derivative of the trend variable functional form of FIG. 5A
involving the ratio of synthetic wavelength trends.
[0024] FIG. 7A shows an exemplary graph of time evolution of a
trend variable functional form involving a single wavelength.
[0025] FIG. 7B shows an exemplary graph of time evolution of the
time derivative of the trend variable functional form of FIG. 6A
involving the single wavelength.
[0026] FIG. 8A shows an exemplary graph of time evolution of a time
derivative of a trend variable functional form involving a single
wavelength.
[0027] FIG. 8B shows an exemplary graph of time evolution of a time
derivative of a trend variable functional form involving a ratio of
synthetic wavelength trends.
[0028] FIG. 8C shows an exemplary graph of time evolution of a time
derivative of a trend variable functional form involving a single
synthetic wavelength, with normalization.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0029] Reference throughout this specification to "one embodiment"
or "an embodiment" means that a particular feature, structure,
material, or characteristic described in connection with the
embodiment is included in at least one embodiment of the
application, but do not denote that they are present in every
embodiment. Thus, the appearances of the phrases "in one
embodiment" or "in an embodiment" in various places throughout this
specification are not necessarily referring to the same embodiment
of the application. Furthermore, the particular features,
structures, materials, or characteristics may be combined in any
suitable manner in one or more embodiments.
[0030] According to an embodiment of the present application,
depicted in FIG. 1 is a plasma etch processing system 10 and a
controller 55, wherein the controller 55 is coupled to the plasma
etch processing system 10. Controller 55 is configured to monitor
the performance of the plasma etch processing system 10 using data
obtained from a variety of sensors disposed in the plasma etch
processing system 10. For example, controller 55 can be used to
control various components of the plasma etch processing system 10,
to detect faults, and to detect an endpoint of an etch process.
[0031] According to the illustrated embodiment of the present
application depicted in FIG. 1, the plasma etch processing system
10 includes a process chamber 15, substrate holder 20, upon which a
substrate 25 to be processed is affixed, gas injection system 40,
and vacuum pumping system 58. Substrate 25 can be, for example, a
semiconductor substrate, a wafer, or an LCD.
[0032] The plasma etch processing system 10 can be, for example,
configured to facilitate the generation of plasma in processing
region 45 adjacent to a surface of substrate 25, where plasma is
formed via collisions between heated electrons and an ionizable
gas. An ionizable gas or mixture of gases is introduced via gas
injection system 40, and the process pressure is adjusted.
Desirably, plasma is utilized to create materials specific to a
predetermined materials process, and to aid the removal of material
from the exposed surfaces of substrate 25. For example, a
controller 55 can be used to control a vacuum pumping system 58 and
gas injection system 40.
[0033] Substrate 25 can be, for example, transferred into and out
of the plasma etch processing system 10 through a slot valve (not
shown) and a chamber feed-through (not shown) via a robotic
substrate transfer system where it is received by substrate lift
pins (not shown) housed within substrate holder 20 and mechanically
translated by devices housed therein. Once substrate 25 is received
from the substrate transfer system, it is lowered to an upper
surface of the substrate holder 20.
[0034] For example, the substrate 25 can be affixed to the
substrate holder 20 via an electrostatic clamping system 28.
Furthermore, the substrate holder 20 can further include a cooling
system including a re-circulating coolant flow that receives heat
from the substrate holder 20 and transfers heat to a heat exchanger
system (not shown), or when heating, transfers heat from the heat
exchanger system. Moreover, gas can be delivered to the back-side
of the substrate via a backside gas delivery system 26 to improve
the gas-gap thermal conductance between the substrate 25 and the
substrate holder 20. Such a system can be utilized when temperature
control of the substrate is required at elevated or reduced
temperatures. For example, temperature control of the substrate can
be useful at temperatures in excess of the steady-state temperature
achieved due to a balance of the heat flux delivered to the
substrate 25 from the plasma and the heat flux removed from
substrate 25 by conduction to the substrate holder 20. In other
embodiments, heating elements, such as resistive heating elements,
or thermo-electric heaters/coolers can be included.
[0035] With continuing reference to FIG. 1, a process gas can be,
for example, introduced to processing region 45 through gas
injection system 40. Process gas can, for example, include a
mixture of gases such as argon, CF.sub.4 and O.sub.2, or Ar, CF and
O.sub.2 for oxide etch applications, or other chemicals, such as,
for example, O.sub.2/CO/Ar/C.sub.4F.sub.8, O.sub.2/CO/Ar/CF.sub.8,
O.sub.2/CO/Ar/C.sub.4F.sub.6, O.sub.2/Ar/C.sub.4F.sub.6,
N.sub.2/H.sub.2. Gas injection system 40 includes a showerhead,
where process gas is supplied from a gas delivery system (not
shown) to the processing region 45 through a gas injection plenum
(not shown) and a multi-orifice showerhead gas injection plate (not
shown).
[0036] As further shown in FIG. 1, the plasma etch processing
system 10, includes a plasma source 80. For example, RF or
microwave power can be coupled from generator 82 through impedance
match network or tuner 84 to the plasma source 80. A frequency for
the application of RF power to the plasma source ranges from 10 MHz
to 200 MHz and is preferably 60 MHz, for capacitively-coupled
(CCP), inductively-coupled (ICP), and transformer-coupled (TCP)
plasma sources. For microwave plasma sources 80, such as electron
cyclotron (ECR) and surface wave plasma (SWP) sources, typical
frequencies of operation of generator 82 are between 1 and 5 GHz,
and preferably about 2.45 GHz. An example of a SWP source 80 is a
radial line slotted antenna (RLSA) plasma source. Moreover, the
controller 55 can be coupled to generator 82 and impedance match
network or tuner 84 in order to control the application of RF or
microwave power to the plasma source 80.
[0037] As shown in FIG. 1, substrate holder 20 can be electrically
biased at an RF voltage via the transmission of RF power from RF
generator 30 through impedance match network 32 to substrate holder
20. The RF bias can serve to attract ions from the plasma formed in
processing region 45, to facilitate the etch process. The frequency
for the application of power to the substrate holder 20 can range
from 0.1 MHz to 30 MHz and is preferably 2 MHz. Alternately, RF
power can be applied to the substrate holder 20 at multiple
frequencies. Furthermore, impedance match network 32 serves to
maximize the transfer of RF power to plasma in process chamber 15
by minimizing the reflected power. Various match network topologies
(e.g., L-type, n-type, T-type, etc.) and automatic control methods
can be utilized.
[0038] Various sensors are configured to receive tool data from the
plasma etch processing system 10. The sensors can include both
sensors that are intrinsic to the plasma etch processing system 10
and sensors extrinsic to the plasma etch processing system 10.
Intrinsic sensors can include those sensors pertaining to the
functionality of the plasma etch processing system 10 such as the
measurement of the Helium backside gas pressure, Helium backside
flow, electrostatic chuck (ESC) voltage, ESC current, substrate
holder 20 temperature (or lower electrode (LEL) temperature),
coolant temperature, upper electrode (UEL) temperature, forward RF
power, reflected RF power, RF self-induced DC bias, RF peak-to-peak
voltage, chamber wall temperature, process gas flow rates, process
gas partial pressures, chamber pressure, capacitor settings (i.e.,
C1 and C2 positions), a focus ring thickness, RF hours, focus ring
RF hours, and any statistic thereof. Alternatively, extrinsic
sensors can include those not directly related to the functionality
of the plasma etch processing system 10, such as, a light detection
device 34 for monitoring the light emitted from the plasma in
processing region 45 as shown in FIG. 1.
[0039] The light detection device 34 may include a detector, such
as, a (silicon) photodiode or a photomultiplier tube (PMT) for
measuring the total light intensity emitted from the plasma. The
light detection device 34 may further include an optical filter,
such as, a narrow-band interference filter. In an alternate
embodiment, the light detection device 34 may include a line CCD
(charge coupled device) or a CID (charge injection device) array
and a light dispersing device such as a grating or a prism.
Additionally, the light detection device 34 may include a
monochromator (e.g., grating/detector system) for measuring light
at a given wavelength, or a spectrometer (e.g., with a rotating or
fixed grating) for measuring the light spectrum. The light
detection device 34 may include a high resolution OES sensor from
Peak Sensor Systems. Such an OES sensor has a broad spectrum that
spans the ultraviolet (UV), visible (VIS) and near infrared (NIR)
light spectrums. In the Peak Sensor System, the resolution is
approximately 1.4 Angstroms, that is, the sensor is capable of
collecting 5550 wavelengths from 240 to 1000 nm. In the Peak System
Sensor, the sensor is equipped with high sensitivity miniature
fiber optic UV-VIS-NIR spectrometers which are, in turn, integrated
with 2048 pixel linear CCD arrays.
[0040] The spectrometers in one embodiment of the present
application receive light transmitted through single and bundled
optical fibers, where the light output from the optical fibers is
dispersed across the line CCD array using a fixed grating. Similar
to the configuration described above, light transmitted through an
optical vacuum window is focused onto the input end of the optical
fibers via a lens or a mirror. Different spectrometers, each
specifically tuned for a given spectral range (UV, VIS and NIR), or
broadband spectrometers covering UV, VIS and NIR, form a sensor for
a process chamber. Each spectrometer includes an independent analog
to digital (A/D) converter. Lastly, depending upon the sensor
utilization, a full emission spectrum can be recorded every 0.01 to
1.0 seconds or faster.
[0041] Alternatively, in an embodiment, a spectrometer with all
reflective optics may be employed by the light detection device 34.
Furthermore, in an embodiment, a single spectrometer involving a
single grating and a single detector for the entire range of light
wavelengths being detected may be used. The design and use of
optical emission spectroscopy hardware for acquiring optical OES
data using e.g. the light detection device 34, are well known to
those skilled in the art of optical plasma diagnostics.
[0042] Controller 55 includes a microprocessor, a memory, and a
digital I/O port (potentially including D/A and/or AD converters)
capable of generating control voltages sufficient to communicate
and activate inputs to the plasma etch processing system 10 as well
as monitor outputs from plasma etch processing system 10. As shown
in FIG. 1, the controller 55 may be coupled to and exchange
information with the RF generator 30, the impedance match network
32, the gas injection system 40, the vacuum pumping system 58, the
backside gas delivery system 26, the electrostatic clamping system
28, and the light detection device 34. A program stored in the
memory is utilized to interact with the aforementioned components
of the plasma etch processing system 10 according to stored process
instructions. One example of controller 55 is a DELL PRECISION
WORKSTATION 530.TM., available from Dell Corporation, Austin, Tex.
Controller 55 may be locally located relative to the plasma etch
processing system 10, or it may be remotely located relative to the
plasma etch processing system 10. For example, the controller 55
may exchange data with the plasma etch processing system 10 using
at least one of a direct connection, an intranet, and the internet.
Controller 55 may be coupled to an intranet at, for example, a
customer site (i.e., a device maker, etc.), or it may be coupled to
an intranet at, for example, a vendor site (i.e., an equipment
manufacturer). Additionally, for example, the controller 55 may be
coupled to the internet. Furthermore, another computer (i.e.,
controller, server, etc.) may, for example, access the controller
55 to exchange data via at least one of a direct connection, an
intranet, and the internet. The controller 55 also implements an
algorithm for detection of an endpoint of an etch process being
performed in the plasma etch processing system 10, based on input
data provided from the light detection device 34, as described
further herein.
[0043] In a plasma etching process, endpoint detection (EPD) using
optical emission spectroscopy is an important technique to control
the etching consistency among wafers. Monitoring a time varying
trend created from one or two selected optical emission
wavelengths, reveals an endpoint, to pause or stop the etching
process. Multivariate data analysis using synthetic wavelengths
helps to improve the SNR and the robustness of the EPD. However, a
synthetic wavelength generated from multivariate data analysis is
generally unable to keep some intrinsic properties of the natural
wavelength, such as being physically meaningful.
[0044] Grouping of the natural wavelengths using a multivariate
model (one non-limiting example of which is PCA) may generate
synthetic wavelengths which enable similar trending as a natural
wavelength for EPD, but with higher SNR endpoint signal. In one
example, non-limiting embodiment of the present application, the
grouping comprises selecting natural wavelengths demonstrating
either constructive or destructive contributions and separate
positive and negative weights for the wavelengths are used in the
grouping of the wavelengths to transform the OES data into the PCA
domain. However, other ways of grouping of the wavelengths may be
used in the generation of the synthetic OES data.
[0045] The process of endpoint determination in accordance with an
embodiment proceeds in two phases. In the first phase, plasma etch
process runs are performed in the plasma processing chamber 15
(step 110 in FIG. 2), and OES data is acquired using the light
detection device 34 during one or more etch processing runs
performed in the plasma etch processing system 10 (step 120), such
that a multivariate model can be established of the acquired OES
data (step 130).
[0046] Once the multivariate model of the OES data has been
established, it can be used in a second phase for in-situ etch
endpoint detection, as long as the etch process being run during
the second phase is reasonably similar in terms of structures being
etched, etch process conditions, etch processing system used, etc.,
to those used in the one or more etch processing runs performed in
the first phase (step 140). This is to ensure the validity of
multivariate model.
[0047] In one non-limiting embodiment of the endpoint determination
(shown in FIG. 3) where the PCA analysis multivariate model is used
with a particular grouping of the natural wavelengths (i.e., with
positive and negative weights), the endpoint detection 200 starts
with etch process runs being performed and OES data being acquired,
using, for example, the light detection device 34. During each
plasma etch process run, spectra is acquired n times (step 210 in
FIG. 3), where n is an integer greater than 1. The sampling
interval between successive OES data acquisitions, i.e. spectra
acquisitions, may vary from 0.01 to 1.0 seconds or faster. Each
acquired OES data set, i.e. spectrum, contains m measured light
intensities corresponding to the m pixels of a CCD detector, each
pixel corresponding to a certain light wavelength projected upon
the pixel by a diffraction grating which is typically employed as a
light dispersion device in light detection device 34. CCD detectors
may have from 256 to 8192 pixels, depending on the desired spectral
resolution, but pixel numbers of 2048 or 4096 are most commonly
used. Two dimensional detectors, for example, having 4k.times.4k
pixels also may be used.
[0048] Next, OES data matrices [X].sup.[i] are set up (step 215)
for all plasma etch process runs i=1, 2, . . . k. Each matrix
[X].sup.[i] is an n.times.m matrix, where acquired spectra is
arranged in rows of the matrix, such that the rows correspond to n
instants in time when OES data is taken, and columns correspond to
the pixel number m. Subsequently, an n.times.m average OES data
matrix [X].sup.avg is optionally calculated (step 220) by averaging
each element of all acquired matrices [X].sup.[i] over all i=1, 2,
. . . k plasma etch process runs. Optional OES spectra
normalization may be performed before calculating the average. When
k=1, there is only a single wafer OES measurement, in which case
there is no average OES matrix computed.
[0049] In one embodiment, the OES data matrix [X].sup.[i] may be
optionally normalized as follows. The OES data matrix [X].sup.[i]
is an n.times.m matrix with components x.sub.ij, where i=1, 2, . .
. n and each row corresponds to an OES snapshot at time t; and j=1,
2 . . . m and each column corresponds to a trend at wavelength
.lamda., so each column is a single wavelength trend. The OES data
normalization may be applied in two ways. In a first way, the
method selects a reference snapshot at time R (i.e., R-throw),
S.sub.R=x.sub.R,j, and then divides every OES data by the reference
snapshot, x.sub.i,j=x.sub.i,j/x.sub.R,j. There can be a single time
snapshot or the snapshot averaged in a period of time. In a second
way, the method selects a reference wavelength .lamda..sub.R (i.e.,
R-th column) and then divides every wavelength by the intensity of
the reference wavelength x.sub.i,j=x.sub.i,j/x.sub.R,j. Similarly,
there can be a single wavelength or an average of certain band of
wavelengths. The inventors have discovered that normalization
addresses intensity drift occurring during the OES runs between
different wafers.
[0050] Subsequently, and as detailed in the '990 patent, noise is
filtered (step 225) from the average OES data matrix [X].sup.avg,
matrices [X].sup.[i] and [X].sup.avg are truncated (step 230) to
remove spectra acquired during plasma startup and optionally
following actual etch process endpoint, and a mean OES data matrix
[S.sub.avg] is computed (step 235), wherein all elements of each
column are set to the average across the entire column (i.e. across
all instants in time) of the elements of the average OES data
matrix [X].sup.avg, and subtracted (step 240) from each acquired
OES data matrix [X].sup.[i] i=1, 2, . . . k, to perform the step of
de-meaning, i.e. average subtraction, prior to constructing a
multivariate model of the acquired OES data.
[0051] Next, in one, non-limiting embodiment, a method, PCA, of
determining the principal components weights [P] (see step 242
between step 240 and step 245 in FIG. 3) used in a multivariate
analysis, to transform the OES data, is described in the following
steps. Other multivariate data analysis methods, for example,
Independent Component Analysis (ICA) method, may be also used. PCA
is an example of unsupervised training method. Other supervised
methods may also be used, as long as target values for each or some
OES spectra are available, such as partial least square (PLS),
support vector machine (SVM) regression or classification methods.
The target values might be obtained from xSEM, transmission
electron microscopy (TEM), optical critical dimension (OCD)
spectroscopy, critical dimension scanning electron microscope
(CDSEM), or other tools.
[0052] During Step 1, the mean spectrum of [X] is subtracted from
each row (step 240 in FIG. 3), but the data is optionally not
normalized using [X]'s standard deviation.
[0053] During Step 2, a covariance matrix
cov(.lamda.)=[.sigma..sup.2.sub.kj] is computed. The covariance
matrix is of m.times.m. For each column (each wavelength), the
average {dot over (x)}.sub.j is calculated. Then, the variance of
column j is:
.sigma..sup.2.sub.jj=.SIGMA..sub.i(xi,j-{dot over (x)}j)(xi,j-{dot
over (x)}j)/n-1,j=1,2 . . . m (1)
[0054] The covariance of row k and column j is:
.sigma..sup.2.sub.kj=.SIGMA..sub.i(xi,j-{dot over (x)}k)(xi,j-{dot
over (x)}j)/n-1,.sigma..sup.2.sub.kj=.sigma..sup.2.sub.jk,j=1,2 . .
. m,k=1,2 . . . m,k.noteq.j (2)
[0055] During Step 3, the eigenvectors and the eigenvalues of the
covariance matrix that satisfy the equation [Covariance
matrix][Eigenvector]=[Eigenvalue][Eigenvector] are calculated. This
is done by performing singular value decomposition of covariance
matrix cov(.lamda.):
P'cov(.lamda.)P=L (3)
where, L is a diagonal matrix of the eigenvalues of cov(.lamda.),
and P is the matrix of the eigenvectors of cov(.lamda.). The
eigenvalues are ordered in descending order, enabling the method to
find the principal components weights in order of significance. For
example, in a particular software, the top three (maximum five)
eigenvectors are used.
[0056] The de-meaned OES data [X].sup.[i]-[S.sub.avg] is then used
as input into the multivariate analysis (step 245), such as for
example, PCA, using the derived weights vector P derived above, to
transform the OES data vector into the PCA domain.
[0057] The inventors have discovered that by grouping the principal
components weights Pj (.lamda.j) into two separate groups
corresponding to positive and negative weighted wavelengths,
separate trends Tj are created, where T.sup.+j+T.sup.-j=Tj. Each
T.sup.+j or T.sup.-j is a single positive trend. Hence, all
conventional trend operations, such as snapshot normalization and
taking a ratio between any of them, can be easily applied to
T.sup.+j and T.sup.-j.
[0058] In one embodiment, the vector [P] is calculated, and
subsequently, the positive vector [P.sup.+] and the negative vector
[P.sup.-] are formed. For example, [P.sup.+] is formed by setting
all negative values in [P] to zero, and [P.sup.-] is formed by
setting all positive values in [P] to zero, and then taking the
absolute value (i.e., converting to positive numbers).
[0059] In step 245, the de-meaned OES data [X].sup.[i]-[S.sub.avg]
along with the determined vector [P] are used to derive the
transformed OES data into the PCA domain
[T.sup.+]=([X]-[S.sub.avg])[P.sup.+] and
[T.sup.-]=([X]-[S.sub.avg])[P.sup.-] (4)
[0060] The method described herein generates synthetic wavelengths
(corresponding to positive and negative weighted natural
wavelengths) to create single signed trends (transformed OES
vectors). For example, positive and negative synthetic wavelengths
are created:
.LAMBDA..sup.+.sub.1=.SIGMA..sub.j=1.sup.n1w.sup.+.sub.jS.sub.j and
.LAMBDA..sup.-.sub.1=.SIGMA..sub.k=1.sup.n2|w.sup.-k|S.sub.k
(5)
where n1 is the number of positive weights and n2 is the number of
negative weights, and
T.sub.1=.SIGMA..sub.i=1.sup.nw.sub.iS.sub.i=.SIGMA..sub.j=1.sup.n1w.sup.-
+.sub.jS.sub.j.SIGMA..sub.k=1.sup.n2|w.sup.-.sub.k|S.sub.k=.LAMBDA..sup.+.-
sub.1-.LAMBDA..sup.-.sub.1. (6)
where, S.sub.i is the intensity of .lamda..sub.i at time t.sub.i,
and T.sub.1, .LAMBDA..sup.+.sub.1 and .LAMBDA..sup.-.sub.1 are all
varying with time.
[0061] Having determined the synthetic wavelengths and the
resulting trends [T.sup.+]=[.LAMBDA..sup.+] and
[T.sup.-]=[.LAMBDA..sup.-], the second phase of the endpoint
detection method is performed by using functional forms of time
evolving values of [T.sup.+] and [T.sup.-]. Since trends [T.sup.+]
and [T.sup.-] are already positive signals, it is possible to
divide each other to get enhanced signals without doing any offset
to shift the trend upward being all positive, and then applying the
offsets to new wafers in real time. For example, in one embodiment,
the ratio T.sup.+.sub.1 (t)/T.sup.+.sub.3 (t) is calculated.
However, in other embodiments, any other functional forms may be
calculated such as squares of the ratio of the synthetic
wavelengths or just a single synthetic wavelength.
[0062] Since the goal of the first phase is to pre-calculate useful
multivariate model parameters for later in-situ etch endpoint
detection, various parameters are saved for later use. In step 250,
the mean OES data matrix [S.sub.avg] is saved to volatile or
non-volatile storage media, to facilitate de-meaning of in-situ
measured OES data. Also in this step, the vector [P] of principal
components (PC) weights is saved to volatile or non-volatile
storage media to facilitate rapid transformation of in-situ
measured OES data into a transformed OES data vector [T].
[0063] In some cases, inventors have discovered that it is useful
for endpoint detection reliability to shift the calculated values
of elements T.sub.i of the transformed OES data vector [T], i.e.
the principal components, as they evolve over time, such that they
concentrate around the value of zero, rather than grow to large
positive or negative values. This shifting is accomplished in step
255, where at least one element T.sub.i of the transformed OES data
vector [T] is evaluated for each instant in time during the etch
process when measurements were taken, and a minimum value of such
element, or elements, min(T), are found. For this purpose,
time-evolving data from the average OES data matrix [X.sub.avg], or
other data, may be used. This minimum value is then stored in step
260 on volatile or non-volatile storage media for later use in
in-situ endpoint detection, whereby the minimum value min (T.sub.i)
of an element T.sub.i of the transformed OES data vector [T] can be
used to shift the time-evolving values of the same element T.sub.i
of the transformed OES data vector [T], calculated from in-situ
measured optical emission spectroscopy (OES) data.
[0064] The stored data values on volatile or non-volatile storage
media are now ready to be used in the second phase, i.e. in in-situ
etch endpoint detection.
[0065] FIG. 4 shows an exemplary flowchart 300 of the process of
in-situ endpoint detection in the plasma etch processing system
100, equipped with light detection device 34, having available the
data saved in steps 250 and 260 of flowchart 200.
[0066] In steps 310 and 315, the previously determined mean OES
data matrix [S.sub.avg] and the vector [P] of principal components
(PC) weights are retrieved from volatile or non-volatile storage
media and loaded into memory of the controller 55 of the plasma
etch processing system 10 of FIG. 1. Controller 55 will perform all
the in-situ calculations needed to determine endpoint of a plasma
process. Also, if used, at least one minimum value min(T.sub.i) of
an element T.sub.i of the transformed OES data vector [T] can be
loaded from volatile or non-volatile media into the memory of
controller 55, in step 320.
[0067] In step 325, a substrate 25 is loaded into the plasma etch
processing system 10 and a plasma is formed in processing region
45.
[0068] In step 330, the light detection device 34 is used to
acquire OES data in-situ, i.e., during the etch process evolving
over time.
[0069] In step 335, the retrieved mean OES data matrix [S.sub.avg]
elements are subtracted from each acquired OES data set, i.e.
spectrum, to de-mean the acquired spectra prior to transformation
using the already developed multivariate model.
[0070] In step 340, the already developed PCA multivariate model is
used to transform the de-meaned OES data into a transformed OES
data vector [T], i.e. the principal components, using Eq. 4 and the
retrieved vector [P] of principal components (PC) weights. This
process is very fast because it involves only a simple
multiplication, and is thus amenable to in-situ real-time
calculation. The computed elements T.sub.i, e.g., natural
wavelengths .LAMBDA..sup.+.sub.i and .LAMBDA..sup.-.sub.i of the
transformed OES data vector [T], as they evolve over time, can be
used for the endpoint detection (step 345).
[0071] In step 350, each time-evolving element T.sub.i of the
transformed OES data vector [T] can be optionally differentiated to
further facilitate the endpoint detection using trend variable
slope data.
[0072] After the time-evolving trend variable has been calculated,
controller 55 of the plasma etch processing system 10 makes a
decision (step 355) whether an endpoint has been reached. If an
endpoint has been reached, the etch process ends at step 360,
otherwise the etch process continues and is continuously monitored
for each endpoint via steps 330-355 of flowchart 300.
[0073] FIG. 5 shows the time evolution of the time derivative of a
trend variable for an etch process. A deep, and thus easily
identified minimum, that the differentiated trend variable goes
through at etch endpoint, is seen. The bottom group of traces
corresponds to the trend obtained using the trend
.LAMBDA..sup.+.sub.1 (t)/.LAMBDA..sup.+.sub.3 (t), in which
synthetic wavelengths were applied. Various traces are shown
corresponding to different wafers used in different etch runs. FIG.
5 also shows the time evolution of other types of trends (for
different wafers), including the time evolution of the time
derivative of a single wavelength trend at .lamda.=656 nm, and also
the time evolution of the time derivative of the ratio of two
single wavelength trends .lamda.=656 nm and .lamda.=777 nm. As seen
in FIG. 5, the endpoint occurs around the 32 second point of the
etch process.
[0074] FIG. 6A shows the time evolution of the trend
.LAMBDA..sup.+.sub.1 (t)/.LAMBDA..sup.-.sub.3 (t) for different
wafers, and FIG. 6B shows the time evolution of the time derivative
of the trend .LAMBDA..sup.+.sub.1 (t)/.LAMBDA..sup.+.sub.3 (t) for
different wafers, in another etch processing run.
[0075] FIG. 7A shows the time evolution of a single wavelength
trend at .lamda.=656 nm for different wafers, and FIG. 7B shows the
time evolution of the time derivative of the single wavelength at
.lamda.=656 nm for different wafers, in another etch processing
run.
[0076] FIG. 8A shows the time evolution of the time derivative of a
single wavelength trend at .lamda.=260 nm for different wafers, and
FIG. 8B shows the time evolution of the time derivative of the
trend obtained using the trend .LAMBDA..sup.+.sub.1
(t)/.LAMBDA..sup.+.sub.3 (t) for different wafers, in another etch
processing run.
[0077] FIG. 8C shows the time evolution of the time derivative of a
synthetic wavelength trend using the normalization discussed above.
The plot refers to a just a synthetic wavelength, not a ratio,
since normalization has been pre-applied.
[0078] After the time-evolving trend variable has been calculated,
controller 55 of the plasma etch processing system 10 needs to make
a decision, in step 355, whether an endpoint has been reached. If
indeed an endpoint has been reached, the etch process is ended at
step 360, otherwise the etch process is continued, and continuously
monitored for etch endpoint via steps 330-355 of flowchart 300.
[0079] Numerous modifications and variations of the present
application are possible in light of the above teachings. It is
therefore to be understood that within the scope of the appended
claims, the application may be practiced otherwise than as
specifically described herein.
* * * * *